Persons in positions of authority or responsibility are often tasked with making important decisions based on available information, the impact of which potentially having far-reaching consequences. These so-called “decision makers” are increasingly in need of insights out of a deluge of the expanding amounts of connected devices, applications, and systems that generate a wide variety of data types from disparate sources and across different formats. As such, decision makers will seek out ways to “reduce time to insight,” and big data clouds will continue to be inadequate for users and nodes that are spread out at the edge of communication networks. Although it is commonly believed that distributed and federated approaches to sensor-related data gathering will eventually transpire, the current approaches and prior work lack the capability to handle the performance requirements at both the system and human levels of edge networks when dealing with complex visual datasets.